Show simple item record

dc.contributor.authorWang, XC
dc.contributor.authorPaliwal, KK
dc.date.accessioned2017-05-03T13:00:54Z
dc.date.available2017-05-03T13:00:54Z
dc.date.issued2003
dc.date.modified2009-09-21T05:49:08Z
dc.identifier.issn0031-3203
dc.identifier.doi10.1016/S0031-3203(03)00044-X
dc.identifier.urihttp://hdl.handle.net/10072/5922
dc.description.abstractFeature extraction is an important component of a pattern recognition system. It performs two tasks: transforming input parameter vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Two popular methods for feature extraction are linear discriminant analysis (LDA) and principal component analysis (PCA). In this paper, the minimum classification error (MCE) training algorithm (which was originally proposed for optimizing classifiers) is investigated for feature extraction. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithm. LDA, PCA, and MCE and GMCE algorithms extract features through linear transformation. Support vector machine (SVM) is a recently developed pattern classification algorithm, which uses non-linear kernel functions to achieve non-linear decision boundaries in the parametric space. In this paper, SVM is also investigated and compared to linear feature extraction algorithms.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherPergamon
dc.publisher.placeUK
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#description
dc.relation.ispartofpagefrom2429
dc.relation.ispartofpageto2439
dc.relation.ispartofjournalPattern Recognition
dc.relation.ispartofvolume36
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleFeature extraction and dimensionality reduction algorithms and their applications in vowel recognition
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2003 Elsevier : Reproduced in accordance with the copyright policy of the publisher : This journal is available online - use hypertext links
gro.date.issued2003
gro.hasfulltextNo Full Text
gro.griffith.authorPaliwal, Kuldip K.


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record